[Numpy-discussion] Catching and dealing with floating point errors

[Bruce Southey]

On 11/08/2010 02:52 PM, Skipper Seabold wrote:
> On Mon, Nov 8, 2010 at 3:42 PM, Bruce Southey<bsouthey at gmail.com>  wrote:
>> On 11/08/2010 02:17 PM, Skipper Seabold wrote:
>>> On Mon, Nov 8, 2010 at 3:14 PM, Skipper Seabold<jsseabold at gmail.com>    wrote:
>>>> I am doing some optimizations on random samples.  In a small number of
>>>> cases, the objective is not well-defined for a given sample (it's not
>>>> possible to tell beforehand and hopefully won't happen much in
>>>> practice).  What is the most numpythonic way to handle this?  It
>>>> doesn't look like I can use np.seterrcall in this case (without
>>>> ignoring its actual intent).  Here's a toy example of the method I
>>>> have come up with.
>>>>
>>>> import numpy as np
>>>>
>>>> def reset_seterr(d):
>>>>      """
>>>>      Helper function to reset FP error-handling to user's original settings
>>>>      """
>>>>      for action in [i+'='+"'"+d[i]+"'" for i in d]:
>>>>          exec(action)
>>>>      np.seterr(over=over, divide=divide, invalid=invalid, under=under)
>>>>
>>> It just occurred to me that this is unsafe.  Better options for
>>> resetting seterr?
>>>
>>>> def log_random_sample(X):
>>>>      """
>>>>      Toy example to catch a FP error, re-sample, and return objective
>>>>      """
>>>>      d = np.seterr() # get original values to reset
>>>>      np.seterr('raise') # set to raise on fp error in order to catch
>>>>      try:
>>>>          ret = np.log(X)
>>>>          reset_seterr(d)
>>>>          return ret
>>>>      except:
>>>>          lb,ub = -1,1  # includes bad domain to test recursion
>>>>          X = np.random.uniform(lb,ub)
>>>>          reset_seterr(d)
>>>>          return log_random_sample(X)
>>>>
>>>> lb,ub = 0,0
>>>> orig_setting = np.seterr()
>>>> X = np.random.uniform(lb,ub)
>>>> log_random_sample(X)
>>>> assert(orig_setting == np.seterr())
>>>>
>>>> This seems to work, but I'm not sure it's as transparent as it could
>>>> be.  If it is, then maybe it will be useful to others.
>>>>
>>>> Skipper
>>>>
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>> What do you mean by 'floating point error'?
>> For example, log of zero is not what I would consider a 'floating point
>> error'.
>>
>> In this case, if you are after a log distribution, then you should be
>> ensuring that the lower bound to the np.random.uniform() is always
>> greater than zero. That is, if lb<= zero then you *know* you have a
>> problem at the very start.
>>
>>
> Just a toy example to get a similar error.  I call x<= 0 on purpose here.
>
>
I was aware of that.

Messing about warnings is not what I consider Pythonic because you 
should be fixing the source of the problem. In this case, your sampling 
must be greater than zero. If you are sampling from a distribution, then 
that should be built into the call otherwise your samples will not be 
from the requested distribution.


Bruce